Method and device for conducting the spectral differentiating, imaging measurement of fluorescent light

ABSTRACT

A method and a device for image producing measurement of fluorescent light, according to which a sample containing fluorophores of different species is irradiated with excitation light of at least one excitation channel defined by its spectral properties. The fluorescent light emitted by the sample is received by at least one detection channel defined by its spectral detection characteristic, and is converted into a digital signal, which is stored for further processing The properties of a number of measuring channels, respectively defined as specific combinations consisting of an excitation channel and a detection channel, are automatically set before conducting the measurement according to the result of a mathematical optimization process, which takes into account the fluorescence characteristics of at least some of the fluorophores presumed by the user to be in the sampler.

CROSS REFERENCE TO RELATED APPLICATION

This application is a national stage of PCT/EP03/05094 filed 15 May 2003and based upon DE 102 22 359.9 filed 21 May 2002 under the InternationalConvention.

FIELD OF THE INVENTION

The invention refers to a method for spectrally differentiating imagingmeasurement of fluorescent light

Furthermore, the invention refers to a device for spectrallydifferentiating imaging measurement of fluorescent light.

BACKGROUND OF THE INVENTION

Such methods and devices find application especially in modern biologytoday. Especially for fluorescence microscopy, a number of specificfluorescence probes have been developed. These are suitable, forexample, for specific labeling of antibodies, certain DNA sequences orother biological structures. Furthermore, they include fusion constructsof certain proteins with fluorescent proteins, such as GFP (GreenFluorescent Protein) or YFP (Yellow Fluorescent Protein), etc.Furthermore, special indicator dyes are included, the fluorescence ofwhich is correlated with the concentration of certain ions, for examplecalcium, with respect to their intensity and/or emission spectrum.

Modern biology attempts to adjust the complexity of the measurementmethods to the complexity of the investigated samples and thus manytimes it is interested in localizing as large a number of differentmarkings in a sample as possible and to resolve these spatially from oneanother.

Another especially current problem is the quantitative determination offluorophores which enter into interaction with one another throughfluorescenceless [sic] energy transfer FRET (Fluorescence ResonanceEnergy Transfer). Such FRET-pairs consisting of donor and acceptorcannot be resolved from one another in an optical microscope. Rather,the superimposition of the donor and acceptor spectra or theirrelationship to one another is measured.

Another current problem is the separation of the fluorescence of anindicator dye into the portions of the bound and free form of thefluorophore for the purposes of obtaining the ratio and subsequentcalculation of the activity of a ligand.

Another problem which occurs in almost all imaging fluorescence methodsin biology is the consideration of the so-called autofluorescence, thatis, the nonspecific background fluorescence which many structureexhibit, such as cells and substrate carriers.

In principle, an essential limitation of this method lies in the factthat the organic fluorophores that are usually used have relativelybroad excitation and emission spectra which is attributed to the largenumber of phononic sublevels that participate in these organicmolecules. Thus it becomes comparatively difficult to excite species offluorophores contained in a sample in a specific way or to detect themspecifically. Rather, usually one obtains a complex composition of thecontributions of different species as signal.

Conventionally, the method employed is to use excitation channels whichare as far away from each other as possible, and to employ as narrowdetection channels as possible. The concept of excitation channel inthis connection is understood to mean the sum of the properties of thelight that excites the fluorescence. This includes especially thespectral properties which also includes in the framework of thisdescription the intensity of the particular spectral components. In anycase, other properties, such as the time of excitation and/or theduration of excitation combined as excitation time, can be used for thedefinition of an excitation channel. Analogously, here the concept ofdetection channel is defined as the sum of the properties of theelements which guide the fluorescent light emitted by the sample andfilter and detect them. This includes again the spectral propertiesincluding the particular sensitivities toward the individual spectralcomponents, and on the other hand the detection time, the detection timepoint and detection duration. Special combinations of excitationchannels and detection channels are described below in a summarizing wayas measurement channels.

In current practice, various methods are known which are aimed atoptimum spectral resolution of the different types of fluorophores inthe presence of one another and depend on the properties of thefluorophores and on their combination. Thus, for example, it is possibleto carry out several recordings in succession with different excitationwavelengths in a given detection channel, where the excitationwavelengths are always chosen so that the absorption maximum of afluorophore species is included as accurately as possible. In this case,one measurement channel is used per measurement. Another possibilityconsists in exciting the sample at an excitation wavelength which liesin the region of the excitation spectra of several fluorophore speciesand to divide the emitted light using filter sets or by cascades of beamsplitters into spectral regions and then introduce these parts toseparate photosensors. Thus, in this method, several measuring channelsare used simultaneously. If the emission or excitation bands of thefluorophores of interest are sufficiently widely separated from oneanother, the frequency regions of the individual measurement channelscan be chosen so that each channel corresponds to a fluorophore.

The disadvantage of these techniques is that mostly a certain cross-talkbetween the channels is unavoidable. This applies especially when anumber of different fluorophores are used in a sample where the spectraoverlap due to the limited bandwidth of usable wavelengths. Althoughthis can be counteracted by sharply limiting the spectral limits of theindividual detection channels, for example, by using narrow band passfilters, the consequence is that a large number of the fluorescencephotons do not contribute to the signal, which has an adverse effect onthe quality of the detected signal. This is especially undesirablebecause, due to bleaching processes of the fluorophores in the sample,the total number of photons that can be emitted by a given preparationis limited, but also because, due to photon noise, the quality andresolution of a measurement becomes better when more photons contributeto the measurement. Although almost all fluorescence photons can be madeuseful by breaking up the emitted fluorescent light spectrally and thentreating the spectrum with the aid of a large number of spectralchannels, the relative noise increases considerably in each individualextremely narrow channel, because only comparatively few photons areavailable for each individual channel, so that this method is onlysuitable in cases of application where the light intensity is especiallyhigh.

The problems addressed above can be reduced greatly when broadmeasurement channels are used, the cross-talk of which is deliberatelytaken into account and the received data are subjected to a considerablemathematical processing or evaluation. For this purpose, the receivedsignals are converted in the detectors or in connected conversion unitsinto digital data, and then these are stored in a memory unit of adigital data processing equipment. In many cases, for example in laserscanning microscopy (LSM), digitalization and subsequent processing ofthe data is an essential component of the technique.

The evaluation of the data mentioned above is usually done with the aidof a computer unit of the digital data processing equipment. Especiallygood results were obtained with the so-called “linear unmixing” method.This method is based on setting up and solving an inhomogeneous linearsystem of equations which, using the known properties of the measurementchannels, establishes a relationship between the measured signal and thefluorophore composition in the sample. This system of equations can berepresented mathematically in a matrix representation as{right arrow over (y)}=A{right arrow over (B)}+{right arrow over(I)}·{right arrow over (b)}  (1)or in a component representation

$\begin{matrix}{y_{r} = {{\sum\limits_{\mu = 1}^{p}{I_{r}a_{\mu\; r}B_{\mu}}} + {I_{r}b_{r}}}} & (2)\end{matrix}$

These formulas are to be understood as follows: The vector {right arrowover (B)} represents the different species of fluorophores in theirrelative concentration at a given image point. Let p be the number ofdifferent fluorophore species. Thus, the vector {right arrow over (B)}has p components B_(μ). The vector {right arrow over (y)} represents thesignal detected in each measurement channel. Let q be the number ofmeasurement channels. Thus, the vector {right arrow over (y)} has qy_(r) components. For example, if four different excitation wavelengthsand four different spectral detection windows were used, the number ofmeasurement channels is q=16. The vector {right arrow over (I)}represents the excitation intensity used for each measurement channeland thus has q components I_(r). The matrix A is the coefficient matrixwhich links the chemical composition {right arrow over (B)} of thefluorophores through the excitation intensities I_(r) of the excitationchannels and the other properties a_(μr) of the measurement channels tothe resulting signal {right arrow over (y)}. Thus, the matrix A has pqelements I_(r)a_(μr). Finally, the vector {right arrow over (b)} with qcomponents b_(r) is a correction quantity which represents the scatteredlight or another background light in each measurement channel. Thequantities B_(μ)are usually to be regarded as location dependent, whilethe other quantities on the right side of equations (1) and (2)represent parameters which are normally the same for all pixels.Autofluorescence of the measured object can be treated either asfluorescence of an additional fluorophore B_(μ)or as background lightb_(r) (in case it is location independent). In the case of FRET, anFRET-pair can be considered as an independent chromophore, theconcentration of which is given through one of the quantities B_(μ).

The goal of“linear unmixing” is to find the solution B of the abovelinear system of equations, which is possible mathematically by simpleinversion of the coefficient matrix A as long as the number of equationsq is greater than or equal to the number of different fluorophorespecies p. For the algorithmic conversion of this mathematicaloperation, a number of numerical methods are known to the person skilledin the art. An explanation of this technique is given in Farkas et al.:“Non-invasive image acquisition and advanced processing in opticalbio-imaging”, Computerized Medical Imaging and Graphics, 22 (1998), p.89-102 or Dickinson et al.: “Multi-spectral imaging and linear unmixingat whole new dimension to laser scanning fluorescent microscopy”,BioTechnics, 31, No. 6 (2001), p. 1272-1278 as well as Boardman:“Inversion of imaging spectroscopy data using singular valuedecomposition”, Proc. IGARSS, 89, No. 4 (1989), p. 2069-2072. Animplementation of this method of evaluation in LSM device was realizedby the company Carl Ziess, Jena, Germany, in theirLaser-Scanning-Microscope LSM 510 meta.

As explained, the method of linear unmixing represents a proven means ofdata evaluation when knowing the properties of the measurement channelsused. However, a disadvantage is that the selection of suitablemeasurement channels, that is, the adjustment of all parameters, such asexcitation wavelength, excitation intensity, excitation time anddetection wavelength and detection time is left to the intuition of theuser, as before. However, since intuition is guided by concrete ruleswhich are obvious to the user, as before, if possible, a fluorophorespecies should be assigned to each measurement channel so that thepossibilities provided by complex data analysis are usually notutilized.

SUMMARY OF THE INVENTION

The task of the present invention is to provide a generic process withwhich the quality of the obtained data is improved in a simple way.

Another task of the present invention is to further develop the genericdevice in such a way that the results obtained with it can be improvedin a simple manner.

These tasks are solved by a method and by a device with thecharacteristics of the independent claims.

Further embodiments of the present invention are given in the dependentclaims.

The invention is based on a generic method by the fact that theproperties of several measurement channels are adjusted automaticallybefore carrying out the measurement, or the user is given thecorresponding instructions for manual adjustment. The automaticadjustments or the instructions are based according to the results of amathematical optimization method carried out by a computer unit of thedigital data processing equipment. In this, the fluorescencecharacteristic of at least some of the fluorophores contained in thesample are taken into consideration. Such a mathematical optimizationmethod is strictly distinct from the automatic control of certainstandard adjustments which can be programmed from the manufacturingplant or can be oriented by a setting library installed by the userhimself. Rather, the user of the data processing equipment enters thefluorophores that he presumes to be present in the sample and theircharacteristics. These data are then used as the basis of themathematical optimization method which calculates the optimized settingsof the measurement channels for the special needs of the user. Thus,especially contrasting to intuition, but based on a mathematicalevaluation of obtained data, a very favorable segmentation of the entirefluorescence spectrum can be performed, instead of cutting out narrowbands with filters and reducing the number of photons contributing tothe signal and reducing the signal quality unnecessarily.

Advantageously, the characteristics of a number of fluorophores arestored in one or several libraries in a memory unit of the digital dataprocessing equipment so that when the user identifies the fluorophorespresumed to be present in the samples, this is sufficient without havingto enter all their complete characteristics.

It is especially favorable when, within the framework of themathematical optimization method, a linear system of equations is set upwhich describes the relationship between the chemical composition of thesample presumed by the user and the signal resulting based on theproperties of the measurement channels to be optimized. Thischaracteristic is based on the mathematical foundation of “linearunmixing”. In any case, this basic idea operates exactly opposite to thestate of the art. Namely, while in the case of “linear unmixing” in thecase of known components Y_(r) and known coefficients a_(μr) thatdescribe the measurement channel and the relative concentrations B_(μ)of the individual fluorophore species are searched for, the aim of themethod according to the invention is rather to vary the coefficientsa_(μr) describing the measurement channels and thereby to carry out anoptimization of the system in the sense that the solution of the systemof equations can be done as uniquely as possible. The optimization isdirected towards the fact that the system of equations can be solved,especially that they are unique. In this way, it can be ensured that theevaluation method which follows the measurement, which is based on themethod of “linear unmixing”, does not fail due to the fact that thecoefficients which describe the measurement channel were chosen in thespecial case so that the number of linear independent equations of thesystem is lower than the number of existing fluorophore species, andthus the system of equations can no longer be solved uniquely.

However, advantageously, the optimization method is designed to be soflexible that it is not optimized exclusively with regard to thesolvability or uniqueness of the solution of the system of equations,but additionally other conditions selected by the user are also takeninto consideration. Examples of such other considerations will beexplained in more detail below.

Advantageously, the optimization method includes the optimization of acondition number of a matrix expression which contains the matrix formedfrom the coefficients of the above-mentioned linear system of equations.Within the framework of the present invention, this is to be understoodso that the algorithmic implementation of the method according to theinvention can be represented mathematically as optimization of acondition number of the said matrix expression. Depending on theconcrete conversion, it can be possible to omit explicit definition of amatrix or of an array within the framework of a computer program.

In an especially preferred manner, the matrix expression on which thecalculation of the condition number is based is formed as matrix producton the left side of the matrix A with its transpose A^(T), that isA^(T)A. This is based on the consideration that the expressions ofequation (1) and (2) can be converted by multiplication on the left sideby A^(T) to obtainA ^(T)({right arrow over (y)}−{right arrow over (I)}·{right arrow over(b)})=A ^(T) A{right arrow over (B)}  (3)

As it is well-known, multiplication on the left with the transposedmatrix makes the obtained matrix expressions symmetric, whichcorresponds to a calculation of most probable values in the sense of theGaussian minimization of the residual sum of squares.

In case of explicit consideration of the noise of the measurementchannels, a matrix weighted by the measurement errors expected by theuser can be employed, which can be regarded as minimization of thequantity χ² (chi square) known from statistics.

Thus, for example, in a preferred embodiment, the condition number to beoptimized corresponds essentially to the determinant of the matrixexpression, especially of the expression A^(T)A. Alternatively,condition numbers which also contain the trace N of the matrixexpression can also be used as optimization criterion. In a furtherpractical example of the method according to the invention, the quantity

$\begin{matrix}{\det/( \frac{N}{n - 1} )^{n - {1/2}}} & (4)\end{matrix}$is used as the condition number to be optimized, where det is thedeterminant, N the trace and n the dimension of the matrix expression.In another advantageous practical example of the method according to theinvention, the ratio of the smallest to the largest eigenvalue of thematrix expression is used as condition number. It was shown that theoptimization of each of these condition numbers leads to a selection ofmeasurement channel properties by variation of the matrix elementsI_(r)a_(μr), which, although oppose intuition in many cases, provideoutstanding results with regard to the data evaluation performed afterthe measurement, especially using the method of “linear unmixing”.

Here, the optimization of different condition numbers leads to resultsof different quality in different constellations of cases. It istherefore especially advantageous to further develop the methodaccording to the invention so that the user is given the possibility togive a global characteristics of the expected measurement result, forexample, very weak fluorescence, especially large number of differentfluorophores which are especially close spectrally, etc., and thus toestablish or to directly establish the condition number to be optimized.

The conversion of the calculated optimization result into therealization of physical properties of the measurement channels can becarried out in many ways. Thus, for example, a selection of a frequencyor frequency band in the excitation and/or detection beam path can beused through adjustable filters, such as AOTFs (Acousto-Optic TunableFilters) or LCTFs (Liquid Crystal Tunable Filters). Similarly, fixedcut-off filters, band-pass filters and/or beam splitters, which arearranged, for example, on motor-driven filter sleds or filter wheels canbe used. Another possibility of automatically influencing themeasurement channel characteristics consists in the variation of theexcitation intensities, for example, by the introduction of theso-called wedge filters in the excitation beam path. The timecharacteristics of the measurement channels can be varied in theconversion of the optimization method according to the invention. Forexample, the duration of excitation can be varied or time detectionwindows can be defined for separation of fluorescent components withshorter and longer life. A number of conversion possibilities are knownto the person skilled in the art in this regard.

Condition numbers of matrices as mentioned above essentially provideestimates of maximum errors, but in practice these can be much smaller.This applies especially when known structures of a given problem aretaken into consideration. In an especially advantageous furtherdevelopment of the method of the invention, therefore the possibility offar-reaching optimization is provided in which the special properties ofthe experimental sources of perturbations are formulated forfluorescence measurements. Especially advantageously, a secondoptimization step is provided in which the noise of the expected signalis optimized by variation of the coefficients that describe theproperties of the measurement channels.

This inventive idea is based on the following recognition. If thecoefficients of matrix A or of the matrix expression A^(T)A aredetermined, preferably optimized by using the first optimization stepdescribed above, the solution of the linear system of equations is asfollows:<{right arrow over (B)}>=(A ^(T) A)³¹ ¹ A ^(T)({right arrow over(y)}−{right arrow over (I)}·{right arrow over (b)})  (5)

Here <{right arrow over (B)}> shows the expectation value of thesolution {right arrow over (B)} in which it is taken into considerationthat the vector {right arrow over (y)}−{right arrow over (I)}·{rightarrow over (b)} has an experimental variation {right arrow over (σ)}.The vector {right arrow over (σ)} is to be understood as component-wisesquare root of the expression σ_(r) ², which are always to be understoodas the expectation values of the variance of the measured value y_(r).These were composed of two components, namely the photon noise, thevariance of which is proportional to the signal level, and the constantdetector noise, which is statistically independent of it, and iscomposed of dark current and the selection noise [read-out noise] of theparticular detector.σ_(r) ² =y _(r) s+σ _(0,r) ²  (6)

Here s is a proportionality constant (a suitably calculated individualphoton contribution) and σ_(0,r) ² is the sum of all constantcontributions to the variance of the signal in channel r.

According to the method of the Gaussian error propagation, the variationσ_(μB) of component B_(μ)of the vector {right arrow over (B)} candescribed by

$\begin{matrix}{\sigma_{B\;\mu}^{2} = {\sum\limits_{r = 1}^{q}{\lbrack \frac{\partial\langle B_{\mu} \rangle}{\partial y_{r}} \rbrack^{2}\mspace{11mu}\sigma_{r}^{2}}}} & (7)\end{matrix}$

Since equation (5) is a linear system of equations and {right arrow over(I)}·{right arrow over (b)} does not depend on y_(r), we have

${\frac{\partial\langle B_{\mu} \rangle}{\partial y_{r}} = c_{\mu\; r}},$where c_(μr) is the element of the r-th line of the μ-th column of thematrix C=(A^(T)A)⁻¹A^(T). Therefore, we have

$\begin{matrix}{\sigma_{B\;\mu}^{2} = {\sum\limits_{r = 1}^{q}{c_{\mu\; r}\sigma_{r}^{2}}}} & (8)\end{matrix}$

This expression or also the sum of all squared deviations

$\begin{matrix}{S_{B}^{2} = {\sum\limits_{\mu = 1}^{p}{\sum\limits_{r = 1}^{q}{c_{\mu\; r}\sigma_{r}^{2}}}}} & (9)\end{matrix}$can be minimized in the space of all measurement channel parameters. Inany case, according to equation (6), the quantities σ_(r) ² contain themeasured values y_(r), so that for the minimization of S_(B) ², the userhas to provide information about the size of the expected signals.

In an especially advantageous embodiment of the method according to theinvention, it is essentially this quantity S_(B) ² which is optimized byvariation of the coefficients describing the measurement channels.

Hereby, it is preferably provided that the noise of the signal to beexpected is optimized with consideration of one or more of additionalconditions that can be introduced by the user. One of these additionalconditions, which can also be used within the framework of the firstoptimization step described above, in an advantageous embodiment of themethod according to the invention, could be a maximum limit for thebleaching of one or several fluorophores. Since as bleaching progresses,the signal will decrease, while certain components of the noise areindependent of time, under certain circumstances the optimization can beperformed with reference to the duration of illumination or itsintensity. As another possible additional condition, the minimization ofthe noise of a signal of a certain intensity, preferably predeterminedby the user, can be utilized with advantage. This additional conditionis especially useful when the expected signal is so low that the totalnoise of the measurement channel is dominated by the dark current and bythe read-out noise of the detector.

As another possible additional condition, in an advantageous embodimentof the method according to the invention, the maximum spectralresolution of the different fluorophores in a given region of apreviously recorded test image can be utilized. This becomes especiallysignificant where one or several different fluorophores are to beresolved with the background of a general non-specific autofluorescenceof the sample or in case a certain image region is of special interestto the user.

In another favorable embodiment of the method according to theinvention, it is provided that the minimization of the relative error ofthe measurement channels be used as an additional condition. This formof additional condition is preferably introduced when ratio measurementsuch as in FRET measurements are to be performed.

It can be especially favorable when the user has the possibility toenter, in addition to one or several additional conditions, or, insteadof that, to enter information to a presumed model of the noise, forexample, based on poison.

In an especially preferred manner, the optimization method according tothe invention is carried out within the framework of an iterative,dialog-controlled process for the definition of additional conditions,which permits the user to enter additional information after performinga preliminary optimization step and to add one or several additionaloptimization steps.

In order to be able to utilize the advantages and specialcharacteristics of the method according to the invention in anespecially advantageous manner, according to the invention a device ismade available, for example, a laser scanning microscope, the digitaldata processing equipment of which is programmed in such a way that thepreviously described optimization process according to the invention canbe performed and which has the above-mentioned technical devices forautomatic adjustment of the measurement channel properties.

BRIEF DESCRIPTION OF THE DRAWING

A preferred embodiment of the method according to the invention and ofthe system according to the invention will be described below with theaid of the attached drawing. The only drawing is

FIG. 1 which shows the schematic structure of a laser scanningmicroscope equipped according to the invention.

DETAILED DESCRIPTION OF THE DRAWINGS

The especially advantageous practical example of the device according tothe invention shown in FIG. 1 is realized as a laser scanningmicroscope. The system consists essentially of three parts, namely adata processing installation 10, a user-interface 20 as well as anoptical/electronic structure 30. The data processing installationincludes a computer unit 11 in which the optimization method accordingto the invention as well as preferably the calculations necessary forthe evaluation of the recorded data are performed. Furthermore, a memoryunit 12 is included in which the recorded data are stored or can bebuffered but also in which the program commands as well as librariesnecessary for carrying out the method according to the invention arestored. The library contains the data necessary for the calculationsaccording to the invention. These are excitation spectra, fluorescencespectra as well as fluorescence lifetimes, number of fluorophores,spectral characteristics of number of filters or color splitters, aswell as the sensitivity characteristics of different detectors. Thespectral and electronic characteristics of different light sources,especially lasers, can be stored in the memory unit 12 of the dataprocessing installation 10.

Furthermore, the data processing installation 10 has a data interface 13through which the recorded data as well as user inputs are fed throughthe user interface 20 into the data processing installation 10 andcontrol commands to adjustable components of the optical/electronicstructure 30 of the device as well as information to the user interface20 can be outputted.

The above elements of the data processing installation 10 can berealized in many ways known to the person skilled in the art and thetechnical details can be adapted to the special particularconfiguration.

In order to measure a fluorescent sample 40, it is placed under themicroscope objective. Through the user interface 20, the user can entervarious data into the data processing installation 10, such as thepresumed chemical fluorophore composition, expected intensities and/oroptimization conditions, for example bleaching limits. Based on thesedata, the computing unit 11 of the data processing installation 10calculates according to the optimization method of the invention thevalues according to which the optical/electronic structure 30 has to beset. As a result of this the measurement channels are defined asspecific combinations of special excitation and detection channels. Inthe practical example shown in FIG. 1, the excitation channels aredesigned comparatively simply. They consist essentially of two lasersources 31 a and 31 b which illuminate the sample 40 through themicroscope objective 39 through motor-controllable collimators 32 a, 32b, deviation mirrors 33 a, 33 b, 33 c, a motor-controllable beamsplitter 34 a, a scanning mirror 35 and a scanning lens 36. Theproperties of the excitation light with respect to wavelength andintensity can be adjusted by controlling laser 31 a, 31 b, collimators32 a, 32 b, as well as the beam splitter wheel 34 a through controllines 131 a, 131 b and 134 a. Naturally, it is within the realm of theinvention to use light sources of other types and/or other numbers, orto adjust the properties of the excitation light with other oradditional controllable components, such as neutral gray filter slides.

The control of the scanning mirror 35 through control line 135 is donein the conventional manner.

The fluorescent light, which is shown in FIG. 1 schematically as adashed line, goes from sample 40 through the microscope objective 39,deviation mirror 33 c, scanning lens 36 and scanning mirror 35 to themotor-controllable beam splitter wheel 34 a. With suitable adjustment ofwheel 34 a, the essential part of the fluorescent light goes through theadjusted beam splitter, while light in the region of the excitationwavelengths is reflected. The setting of the detection channels is doneby adjusting this beam splitter wheel 34 a as well as by adjusting otherbeam splitter wheels 34 b and 34 c, which are adjusted through thecontrol lines 134 b and 134 c according to the parameters determined bythe optimization method according to the invention. Another channelspecification is done through the setting of the filter wheels 36 a and36 b, which are adjusted through control lines 136 a and 136 b accordingto the parameters determined by the optimization method according to theinvention.

The fluorescent light thus preselected falls on various detectors 37 a,37 b and 37 c, the data of which are fed through the input lines 137 a,137 b and 137 c into the data processing installation 10. Depending onthe special construction, the data are digitized already in detectors 37a, 37 b, 37 c, or only in the data interface 13 of the data processinginstallation 10. The data thus recorded and stored in the memory unit 12of the digital processing installation 10 are evaluated by the computerunit 11 using known data evaluation programs, where preferably themethod of “linear unmixing” finds application.

The adjustment or definition of the measurement channels according tothe invention refers to the practical example, to the spectralsectioning of the fluorescent light as well as to the number ofmeasurement channels used, that is, the number of combinations ofexcitation and detection channels used. Here the number and nature ofthe detectors is as variable as the light sources are. Especially, notshown in FIG. 1, detectors can be used which can be controlled regardingtheir detection time, that is, detection duration and/or detection timepoint according to calculated optimization parameters. Naturally, it isalso possible to create the pinholes 38 a-c necessary for the LSMstructure and to include their diameter in the series of optimizationparameters.

Naturally, the embodiment described and shown in FIG. 1 of a deviceaccording to the invention is only an illustration of an example of anespecially advantageous variant. However, many variations can beconceived within the realm of the present invention.

Reference Number List  10 Digital data processing installation  11Computing unit of 10  12 Memory unit of 10  13 Data interface of 10 120Control lines 131a,b Control lines 134a-c Control lines 135 Controllines 136a,b Control lines 137a-c Control lines  20 User interface  30Optical/electronic structure  31a,b Laser  32a,b Collimator  33a-cDeviation mirror  34a-c Beam splitter wheel  35 Scanning mirror  36Scanning lens  37a-c Detector  38a-c Pinhole  39 Microscope objective 40 Fluorescent sample

1. A method for an image producing measurement of fluorescent light,comprising: irradiating a sample, containing fluorophores of at leastone species, with exciting light of at least one excitation channeldefined by its spectral properties, and receiving the fluorescent lightemitted by the sample by at least one detection channel defined by itsspectral detection characteristic and converting it into a digitalsignal, storing the digital signal in a memory unit of a digital dataprocessing unit, and producing an image or image file of the samplebased on the digital signal, wherein, prior to carrying out ameasurement, properties of the at least one excitation channel and theat least one detection channel are predetermined according to a resultof a mathematical optimization process carried out by a computing unitof the digital data processing unit, which takes into considerationfluorescence characteristics of at least one of the fluorophorespresumed by a user to be in the sample, wherein the optimization processincludes a) setting up a linear system of equations, coefficients ofwhich represent spectral characteristics of measurement channels, whichare respectively defined as a combination of one excitation channel andone detection channel, and which describes a relationship between auser-presumed chemical composition of the sample and a signal calculatedbased on the measurement channel characteristics to be optimized, b)varying the coefficients, until a unique solution to the equation systembecomes possible, c) the coefficients calculated as a result of theoptimization process forming a basis of the predetermination of theproperties of the excitation and detection channels.
 2. The methodaccording to claim 1, wherein the uniqueness of the solution of thelinear system of equations is optimized taking into consideration one ormore additional conditions that can be introduced by the user.
 3. Themethod according claim 1, wherein the mathematical optimization methodincludes the optimization of a condition number of a matrix expressioncontaining a matrix formed from the coefficients of the linear system ofequations.
 4. The method according to claim 1, wherein the mathematicaloptimization method contains a second optimization step in which a noiseof an expected signal is optimized by variation of coefficients whichdescribe properties of the measurement channels.
 5. The method accordingto claim 2, wherein a maximum limit or an optimum for the bleaching ofone or several fluorophores is used as additional condition.
 6. Themethod according to claim 2, wherein a minimization of the noise of asignal of a given intensity is used as additional condition.
 7. Themethod according to claim 2, wherein a maximum spectral resolution ofdifferent fluorophores in a region of a previously recorded test imageis used as additional condition.
 8. The method according to claim 2,wherein a minimization of a relative error is used as additionalcondition.
 9. The method according to claim 2, wherein the optimizationmethod includes an iterative, dialog-controlled process for a definitionof the additional conditions.
 10. The method according to claim 2,wherein an automatic adjustment of measurement channel properties iscarried out by starting and/or motor motion of AOTFs, LCTFs, cut-offfilters, band pass filters or neutral gray filters and/or beamsplitters.
 11. The method according to claim 3, wherein matrix elementsof the matrix expression are weighted for minimization of a quantity χ²corresponding to measurement errors expected by the user.
 12. The methodaccording to claim 3, wherein the matrix expression containing thecoefficient matrix is essentially a left-side matrix product of thecoefficient matrix with its transposed form.
 13. The method according toclaim 3, wherein the condition number contains essentially thedeterminant of the matrix expression.
 14. The method according to claim3, wherein the condition number contains essentially the trace of thematrix expression.
 15. The method according to claim 3, wherein thecondition number corresponds essentially to a quantity${\det/( \frac{N}{n - 1} )^{{({n - 1})}/2}},$ where det isthe determinant, N is the trace and n is the dimension of the matrixexpression.
 16. The method according to claim 3, wherein the conditionnumber contains essentially a ratio of the smallest to the largestintrinsic value of the matrix expression.
 17. The method according toclaim 3, wherein the condition number to be optimized can be chosen bythe user.
 18. The method according to claim 17, wherein the conditionnumber to be optimized is chosen by the user by entering a globalcharacterization of a signal to be expected into the digital dataprocessing unit and then the condition number is determinedautomatically.
 19. The method according to claim 4, wherein the noise ofthe signal to be expected is optimized taking into consideration one ormore additional conditions that can be introduced by the user.